Answer selection in a computerized question answering system refers to selecting a correct answer to a question from some candidate answers. According to an existing method, it is mainly attempted to generate high-quality sentence distributed representations for questions and candidate answers, and these distributed representations will be then used to measure a correlation between the candidate answers and the question and further select and return a candidate answer with the highest correlation as the correct answer. The existing method is performed mostly through a Recurrent Neural Network (RNN) to achieve a good performance However, the inventors have found during use that the RNN processes all sentences of question and answer by using a same feature extractor regardless of sentence lengths. A long-term dependency problem often occurs during the use of these methods, which means that it is very difficult for the network to learn a dependency relationship between far-spaced words. Because a long-distance interaction between words of these sentences cannot be completely captured, the quality is very low at the time of a long sentence distributed representation, leading to loss of global information.
Therefore, a heretofore unaddressed need exists in the art to address the aforementioned deficiencies and inadequacies.